




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCamelCase							:						int					       =  False







class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    int		,				A_   :    Dict=32    )  ->      Any:

       """simple docstring"""






       set_seed(0    )
       lowerCamelCase_							=       UNetaDModel(sample_size=A_		,				in_channels=3		,				out_channels=3    )
       lowerCamelCase_							=       torch.optim.SGD(model.parameters()		,				lr=0.0001    )
       return model, optimizer





 @slow
 def 							a__					(							self   :    int    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       lowerCamelCase_							=       DDPMScheduler(
           num_train_timesteps=1000		,				beta_start=0.0001		,				beta_end=0.02		,				beta_schedule='linear'		,				clip_sample=A_		,				)
       lowerCamelCase_							=       DDIMScheduler(
           num_train_timesteps=1000		,				beta_start=0.0001		,				beta_end=0.02		,				beta_schedule='linear'		,				clip_sample=A_		,				)

       assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

       # shared batches for DDPM and DDIM
       set_seed(0    )
       lowerCamelCase_							=       [torch.randn((4, 3, 32, 32)    ).clip(-1		,				1    ).to(A_    ) for _ in range(4    )]
       lowerCamelCase_							=       [torch.randn((4, 3, 32, 32)    ).to(A_    ) for _ in range(4    )]
       lowerCamelCase_							=       [torch.randint(0		,				1000		,				(4,)    ).long().to(A_    ) for _ in range(4    )]

       # train with a DDPM scheduler
       lowerCamelCase_	,							lowerCamelCase_							=       self.get_model_optimizer(resolution=32    )
       model.train().to(A_    )
       for i in range(4    ):
             optimizer.zero_grad()
             lowerCamelCase_							=       ddpm_scheduler.add_noise(clean_images[i]		,				noise[i]		,				timesteps[i]    )
             lowerCamelCase_							=       model(A_		,				timesteps[i]    ).sample
             lowerCamelCase_							=       torch.nn.functional.mse_loss(A_		,				noise[i]    )
             loss.backward()
             optimizer.step()
       del model, optimizer

       # recreate the model and optimizer, and retry with DDIM
       lowerCamelCase_	,							lowerCamelCase_							=       self.get_model_optimizer(resolution=32    )
       model.train().to(A_    )
       for i in range(4    ):
             optimizer.zero_grad()
             lowerCamelCase_							=       ddim_scheduler.add_noise(clean_images[i]		,				noise[i]		,				timesteps[i]    )
             lowerCamelCase_							=       model(A_		,				timesteps[i]    ).sample
             lowerCamelCase_							=       torch.nn.functional.mse_loss(A_		,				noise[i]    )
             loss.backward()
             optimizer.step()
       del model, optimizer

       self.assertTrue(torch.allclose(A_		,				A_		,				atol=1E-5    )    )
       self.assertTrue(torch.allclose(A_		,				A_		,				atol=1E-5    )    )




"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
